The use of machine learning in embedded systems is an interesting topic, especially with the growth in popularity of the Internet of Things (IoT). The capacity of a system, such as a robot, to self-localize, is a fundamental skill for its navigation and decision-making processes. This work focuses on the feasibility of using machine learning in a Raspberry Pi 4 Model B, solving the localization problem using images and fiducial markers (ArUco markers) in the context of the RobotAtFactory 4.0 competition. The approaches were validated using a realistically simulated scenario. Three algorithms were tested, and all were shown to be a good solution for a limited amount of data. Results also show that when the amount of data grows, only Multi-Layer Perception (MLP) is feasible for the embedded application due to the required training time and the resulting size of the model.
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This paper introduces the Internet-of-Things (IoT) and describes its evolution from a concept proposed by Kevin Ashton in 1999 through its public emergence in 2005 in a United Nations ITU report entitled “The Internet of Things”, to the present day where IoT devices are available as off-the-shelf products from major manufacturers. Using a systematic study of public literature, the paper presents a five-phase categorisation of the development of the Internet-of-Things from its beginnings to the present day. Four mini case studies are included to illustrate some of the issues involved. Finally, the paper discusses some of the big issues facing future developers and marketers of Internet-of-Things based products ranging from artificial intelligence (AI) through to customer privacy and acceptance finishing with an optimistic assessment of the future of the Internet-of-Things.
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